Rashan Aasiyah, Püttmann Daniel P, de Keizer Nicolette F, Dongelmans Dave A, Cornet Ronald, Ranzani Otavio, Waweru-Siika Wangari, Smith Matthew, Harris Steve, Beane Abi, Bakhshi-Raiez Ferishta
Institute of Health Informatics, University College London, London WC1E 6BT, United Kingdom.
Department of Medical Informatics, Amsterdam Public Health Institute, Amsterdam UMC, University of Amsterdam, Amsterdam 1105 AZ, The Netherlands.
JAMIA Open. 2025 Jul 22;8(4):ooaf052. doi: 10.1093/jamiaopen/ooaf052. eCollection 2025 Aug.
Federated analysis is a method that allows data analysis to be performed on similar datasets without exchanging any data, thus facilitating international research collaboration while adhering to strict privacy laws. This study aimed to evaluate the feasibility of using federated analysis to benchmark mortality in 2 critical care quality registry databases converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), describing challenges to and recommendations for performing federated analysis on data transformed to OMOP CDM.
To identify as many challenges as possible and to be able to complete the benchmarking phase, a 2-step approach was taken during implementation. The first step was a naive implementation to allow challenges to surface naturally; the second step was developing solutions for the encountered challenges. Expected patient mortality risk was calculated by applying the Acute Physiology and Chronic Health Evaluation II (APACHE II) model to data from OMOP CDM databases containing adult ICU encounters between July 1, 2019 and December 31, 2022. An analysis script was developed to calculate comparable, registry level standardized mortality ratios. Challenges were recorded and categorized into predefined categories: "data preparation," "data analysis plan," and "data interpretation." Challenges specific to the OMOP CDM were further categorized using published steps from an existing generic harmonization process.
A total of 7 challenges were identified, 4 of which were related to data preparation, 1 to data analysis, and 1 to data interpretation. Out of all 7 challenges, 4 stemmed from decisions made during the implementation of OMOP CDM. Several recommended solutions were distilled from the naive approach.
Federated analysis facilitated by a CDM is a feasible option for critical care quality registries. However, future analysis is influenced by decisions made during the CDM implementation process. Thus, prior publication of data dictionaries and the use of metadata to communicate data handling and data source classification during CDM implementation will improve the efficiency and accuracy of subsequent analysis.
联合分析是一种允许在不交换任何数据的情况下对相似数据集进行数据分析的方法，从而在遵守严格隐私法的同时促进国际研究合作。本研究旨在评估使用联合分析对两个转换为观察性医疗结局合作组织（OMOP）通用数据模型（CDM）的重症监护质量登记数据库中的死亡率进行基准测试的可行性，描述对转换为OMOP CDM的数据进行联合分析的挑战和建议。
为了尽可能多地识别挑战并能够完成基准测试阶段，在实施过程中采用了两步法。第一步是进行简单的实施，让挑战自然浮现；第二步是为遇到的挑战开发解决方案。通过将急性生理与慢性健康状况评估II（APACHE II）模型应用于2019年7月1日至2022年12月31日期间包含成人重症监护病房就诊记录的OMOP CDM数据库中的数据，计算预期患者死亡风险。开发了一个分析脚本以计算可比的登记级别标准化死亡率。记录挑战并将其分类为预定义类别：“数据准备”、“数据分析计划”和“数据解释”。使用现有通用协调过程中公布的步骤，进一步对特定于OMOP CDM的挑战进行分类。
共识别出7个挑战，其中4个与数据准备有关，1个与数据分析有关，1个与数据解释有关。在所有7个挑战中，4个源于OMOP CDM实施过程中做出的决策。从简单方法中提炼出了几个推荐的解决方案。
由CDM推动的联合分析对于重症监护质量登记来说是一种可行的选择。然而，未来的分析会受到CDM实施过程中所做决策的影响。因此，在CDM实施过程中预先公布数据字典并使用元数据来传达数据处理和数据源分类，将提高后续分析的效率和准确性。
